International Journal of Electrical Power System and Technology

A Survey on Recent Progress in Transmission Congestion Management

  1. Swati K. Warungase
  2. M. V. Bhatkar

Abstract

After deregulating their electrical sectors recently, a number of nations, including Chile, Peru, the United States, the United Kingdom, the European Union, Canada, Colombia, New Zealand, Scandinavia, and Australia, replaced their once-monolithic public utilities with competitive power markets. The electricity industry has been changing around the world over the last few decades, and as a result, network congestion is unavoidable. Market failure, transmission capacity limitations violations, and excessive energy prices, jeopardizing the reliability and security of power networks can be caused by Congestion. Congestion may also result in unanticipated pricing discrepancies in electricity markets, resulting in market power. When the network is congested, the major concern of an independent system operator (ISO) in a deregulated power market (DPM) is to preserve the power market's stability and safety by increasing market efficiency. As a result, in DPM and the power system, congestion management (CM) is crucial. This study does a survey of congestion management approaches in order to compile every part of latest DPM papers. Its goal is to provide readers with a summary of advanced CM approaches as well as classic CM methods which described already. We conducted a comparative study of the several famous CM approaches in this work.

Keywords: Available Transfer Capability, Congestion Management, Demand Response Management, Transmission System Operator

Keywords

References (101)

  1. [1] A. Kumar, S. C. Srivastava, and S. N. Singh, “Congestion man- agement in competitive power market: A bibliographical survey,” Electr. Power Syst. Res., vol. 76, pp. 153–64, 2005, https://doi.org/ 10.1016/j.epsr.2005.05.001.
  2. [2] O. B. Fosso, A. Gjelsvik, A. Haugstad, B. Mo, and I. Wangensteen, “Generation scheduling in a deregulated system. The Norwegian case,” IEEE Trans. Power Syst., vol. 14, no. 1, pp. 75–81, Feb. 1999, https://doi.org/10.1109/59.744487.
  3. [3] I. Rahman and J. Mohamad-Saleh, “Hybrid bio-Inspired compu- tational intelligence techniques for solving power system optimi- zation problems: A comprehensive survey,” Appl. Soft Comput. J., vol. 69, pp. 72–130, 2018, https://doi.org/10.1016/j.asoc.2018.04. 051
  4. [4] B. V. Manikandan, S. Charles Raja, P. Venkatesh, and M. Mandala, “Comparative study of two congestion management methods for the restructured power systems,” J. Electr. Eng. Technol., vol. 6, no. 3, pp. 302–10, 2011, https://doi.org/10.5370/ JEET.2011.6.3.302
  5. [5] S. Gumpu, B. Pamulaparthy, and A. Sharma, “Review of conges- tion management methods from conventional to smart grid sce- nario,” Int. J. Emerging Electric Power Syst., vol. 20, no. 3, p. 20180265, 2019, https://doi.org/10.1515/ijeeps-2018-0265
  6. [6] A. Narain, S. K. Srivastava, and S. N. Singh, “Congestion man- agement approaches in restructured power system: Key issues and challenges,” Electr. J., vol. 33, no. 3, p. 106715, 2020, ISSN 1040- 6190, https://doi.org/10.1016/j.tej.2020.106715
  7. [7] S. Prabhakar Karthikeyan, I. Jacob Raglend, and D. P. Kothari, “Impact of FACTS devices on exercising market power in deregulated electricity market,” Front. Energy, vol. 7, pp. 448–55, 2013, https://doi.org/10.1007/s11708-013-0262-x
  8. [8] L.L. Lai, Power System Restructuring and Deregulation Trading, Performance and Information Technology, Wiley, November 2001, ISBN: 978-0-471-49500-0
  9. [9] N. I. Yusoff, A. A. M. Zin, and A. Bin Khairuddin, “Congestion management in power system: A review,” in 3rd Int. Conf. Power Gener. Syst. Renew. Energy Technol. PGSRET 2017, vol. 2018- January, 2018, pp. 22–7, https://doi.org/10.1109/PGSRET.2017. 8251795.
  10. [10] M. Gupta, V. Kumar, G. K. Banerjee, and N. K. Sharma, “Miti- gating congestion in a power system and role of FACTS devices,” Adv. Electr. Eng., vol. 2017, pp. 1–7, 2017, https://doi.org/10.1155/ 2017/4862428.
  11. [11] J. Brosda and E. Handschin, “Congestion management methods with a special consideration of FACTS-devices,” in 2001 IEEE Porto Power Tech Proceedings, 2001, https://doi.org/10.1109/PTC. 2001.964593.
  12. [12] ] R. S. Rao and V. S. Rao, “Electrical power and energy systems a generalized approach for determination of optimal location and performance analysis of FACTs devices,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 711–24, 2015, https://doi.org/10.1016/j. ijepes.2015.06.004.
  13. [13] Y. Kishore and G. M. Baleboina, “Enhancement of voltage stability and transmission congestion management with UPFC,” Int. J. Grid Distrib. Comput., vol. 11, no. 6, pp. 15–26, November, 2018,
  14. https://doi.org/10.14257/ijgdc.2018.11.6.02.
  15. [14] B. Chong, X. P. Zhang, L. Yao, K. R. Godfrey, and M. Bazargan, “Congestion management of electricity markets using FACTS controllers,” in 2007 IEEE Power Engineering Society General Meeting, 2007, pp. 1– 6, https://doi.org/10.1109/PES.2007. 386122.
  16. [15] A. Pillay, S. Prabhakar Karthikeyan, and D. P. Kothari, “Conges- tion management in power systems: A review,” Int. J. Electr. Power Energy Syst., vol. 70, pp. 83–90, 2015, https://doi.org/10.1016/j. ijepes.2015.01.022
  17. [16] P. Acharjee, “Optimal power flow with UPFC using security constrained self-adaptive differential evolutionary algorithm for restructured power system,” Int. J. Electr. Power Energy Syst., vol. 76, pp. 69–81, 2016, https://doi.org/10.1016/j.ijepes.2015.09.025.
  18. [17] H. Besharat and S. A. Taher, “Congestion management by determining optimal location of TCSC in deregulated power sys- tems,” Int. J. Electr. Power Energy Syst., vol. 30, no. 10, pp. 563–8, 2008, https://doi.org/10.1016/j.ijepes.2008.08.007.
  19. [18] [18] R. Benabid, M. Boudour, and M. A. Abido, “Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization,” Electr. Power Syst. Res., vol. 79, no. 12, pp. 1668–77, 2009, https://doi.org/10.1016/j.epsr.2009.07.004.
  20. [19] M. Esmaili, H. A. Shayanfar, and R. Moslemi, “Locating series FACTS devices for multi-objective congestion management improving voltage and transient stability,” Eur. J. Oper. Res., vol. 236, no. 2, pp. 763–73, 16 July 2014, https://doi.org/10.1016/j.ejor. 2014.01.017.
  21. [20] S. A. Taher and M. Karim Amooshahi, “Optimal placement of UPFC in power systems using immune algorithm,” Simul. Model. Pract. Theory, vol. 19, no. 5, pp. 1399–412, 2011, https://doi.org/ 10.1016/j.simpat.2011.03.001.
  22. [21] S. A. Taher and M. K. Amooshahi, “New approach for optimal UPFC placement using hybrid immune algorithm in electric po- wer systems,” Int. J. Electr. Power Energy Syst., vol. 43, no. 1, pp. 899–909, 2012, https://doi.org/10.1016/j.ijepes.2012.05.064.
  23. [22] S. N. Singh and A. K. David, “Optimal location of FACTS devices for congestion management,” Electr. Power Syst. Res., vol. 58, no. 2, pp. 71–9, 2001, https://doi.org/10.1016/j.jpba.2017.03.034.
  24. [23] A. Kumar, S. N. Singh, and L. L. Lai, “Impact of TCPAR on congestion clusters and congestion management using mixed integer nonlinear programming approach,” in Power Engineering Society General Meeting, 2006. IEEE, 2006, https://doi.org/10. 1109/PES.2006.1709109.
  25. [24] A. Mishra and G. Venkata Nagesh Kumar, “Congestion man- agement of deregulated power systems by optimal setting of Interline Power Flow Controller using Gravitational Search algo- rithm,” J. Electr. Syst. Inf. Technol., vol. 4, no. 1, pp. 198–212, May 2017, https://doi.org/10.1016/j.jesit.2016.09.001.
  26. [25] A. Mishra and G. V. Nagesh Kumar, “Congestion management of power system with interline power flow controller using disparity line utilization factor and multi-objective differential evolution,” CSEE J. Power Energy Syst., vol. 1, no. 3, pp. 76–85, 2015, https:// doi.org/10.17775/CSEEJPES.2015.00038.
  27. [26] R. A. Hooshmand, M. J. Morshed, and M. Parastegari, “Conges- tion management by determining optimal location of series FACTS devices using hybrid bacterial foraging and Nelder-Mead algorithm,” Appl. Soft Comput. J., vol. 28, no. C, pp. 57–68, March 2015, https://doi.org/10.1016/j.asoc.2014.11.032.
  28. [27] S. K. Behera and N. K. Mohanty, “Congestion management using thyristor controlled series compensator employing Improved Grey Wolf Optimization technique,” Int. J. Electr. Eng. Educ., vol. 58, no. 2, pp. 179–99, https://doi.org/10.1177/0020720918822730.
  29. [28] A. Sharma and S. Jain, “Locating series FACTS devices for man- aging transmission congestion in deregulated power market,” In- dia Int. Conf. Power Electron. IICPE, vol. 2016, November, 2017, https://doi.org/10.1109/IICPE.2016.8079445.
  30. [29] A. K. R. K. and S. P. Singh, “Congestion mitigation using UPFC,” IET Gener. Transm. Distrib., vol. 10, no. 10, pp. 2433–42, 2016, https://doi.org/10.1049/iet-gtd.2015.1199.
  31. [30] C. Venkaiah and D. M. Vinod Kumar, “Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power rescheduling of generators,” Appl. Soft Comput. J., vol. 11, no. 8, pp. 4921–30, 2011, https://doi.org/10.1016/j.asoc.2011.06.007.
  32. [31] S. Chanda and A. De, “Application of particle swarm optimization for relieving congestion in deregulated power system,” in 2011 IEEE Recent Advances in Intelligent Computational Systems, 2011, pp. 837–40, https://doi.org/10.1016/j.asoc.2011.06.007.
  33. [32] S. Hajforoosh, S. M. H. Nabavi, and M. A. S. Masoum, “Coordinated ggregated-based particle swarm optimisation algorithm for congestion management in restructured power market by placement and sizing of unified power flow controller,” IET Sci. Meas.Technol., vol. 6, no. 4, p. 267, 2012, https://doi.org/10.1049/ietsmt. 2011.0143.
  34. [33] S. K. Joshi and K. S. Pandya, “Active and reactive power rescheduling for congestion management using particle swarm optimization,” AUPEC 2011, 2011, pp. 1–6.
  35. [34] H. A. Shayanfar, H. Shayeghi, and A. Ghasemi, “SPSO-TVAC congestion management in a practical electricity market based generator sensitivity,” in Proceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014 – WORLDCOMP 2014, 2014.
  36. [35] P. Boonyaritdachochai, C. Boonchuay, and W. Ongsakul, “Optimal congestion management in electricity market using particle swarm optimization with time varying acceleration coefficients,” AIP Conf. Proc., vol. 1239, no. 4, pp. 382–7, 2010, https://doi.org/10.1063/1.3459776.
  37. [36] N. Kinhekar, N. P. Padhy, and H. O. Gupta, “Particle swarm optimization based demand response for residential consumers,” IEEE Power Energy Soc. Gen. Meet., vol. 2015-September, pp. 1–5, 2015, https://doi.org/10.1109/PESGM.2015.7286466.
  38. [37] H. Farahmand, M. Rashidinejad, A. Mousavi, A. A. Gharaveisi, M. R. Irving, and G. A. Taylor, “Hybrid mutation particle swarm optimisation method for available transfer capability enhancement,” Int. J. Electr. Power Energy Syst., vol. 42, no. 1, pp. 240–9,2012, https://doi.org/10.1016/j.ijepes.2012.04.020.
  39. [38] I. Batra and S. Ghosh, “A novel approach of congestion management in deregulated power system using an advanced and intelligently trained twin extremity chaotic map adaptive particle swarm optimization algorithm,” Arab. J. Sci. Eng., vol. 44, pp. 6861–86, 2019, https://doi.org/10.1007/s13369-018-3675-3.
  40. [39] I. Batra and S. Ghosh, “An improved tent map-adaptive chaotic particle swarm optimization (ITM-CPSO) – based novel approach toward security constraint optimal congestion management,” Iran. J. Sci. Technol. Trans. Electr. Eng., vol. 6, 2018, ttps://doi.org/10.1007/s40998-018-0072-6.
  41. [40] M. Sarwar and A. S. Siddiqui, “An efficient particle swarm optimizer for congestion management in deregulated electricity market,” J. Electr. Syst. Inf. Technol., vol. 2, no. 3, pp. 269–82,2015, https://doi.org/10.1016/j.jesit.2015.08.002.
  42. [41] K. Ravi and M. Rajaram, “Electrical power and energy systems optimal location of FACTS devices using improved particle swarm optimization,” Int. J. Electr. Power Energy Syst., vol. 49, pp. 333–8, .2013, https://doi.org/10.1016/j.ijepes.2012.12.008.
  43. [42] A. Ahamed Jeelani Basha, M. Anitha, E. B. Elanchezhian, “Application of firefly algorithm for congestion management problem in the deregulated electricity market,” Int. J. Intell. Eng. Inform., vol. 6, pp. 222–41, 2018
  44. [43] T. Bhattacharjee and A. K. Chakraborty, “Congestion management in a deregulated power system using NSGAII,” in 2012 IEEE 5th Power India Conference, PICONF 2012, 2012, https://doi.org/ 10.1109/PowerI.2012.6479529.
  45. [44] S. Ushasurendra, “Congestion management in deregulated power sector using fuzzy based optimal location technique for series flexible alternative current transmission system (FACTS) device,” J. Electr. Electron. Eng. Res., vol. 4, no. 1, pp. 12–20, 2012, https://doi.org/10.3317/jraas.2003.021.
  46. [45] S. N. Pandey and S. Tapaswi and L. Tapaswi, “Growing RBFNN- based soft computing approach for congestion management,” Neural. Comput Applic., vol. 18, p. 945, 2009 https://doi.org/10. 1007/s00521-008-0205-3.
  47. [46] O. Abedinia, N. Amjady, and M. S. Naderi, “Optimal congestion management in an electricity market using Modified Invasive Weed Optimization,” in 2012 11th International Conference on Environment and Electrical Engineering, EEEIC 2012 – Conference Proceedings, 2012, https://doi.org/10.1109/EEEIC.2012.6221423.
  48. [47] D. Mende, D. S. Stock, T. Hennig, L. Lower, and L. Hofmann, “Multiobjective optimization in congestion management considering technical and economic aspects,” in Asia-Pacific Power and Energy Engineering Conference, APPEEC, 2016, https://doi.org/10.1109/APPEEC.2016.7779711.
  49. [48] S. S. Reddy, “Multi-objective based congestion management using generation rescheduling and load shedding,” IEEE Trans. Power Syst., vol. 32, no. 2, pp. 852–63, 2017, https://doi.org/10.1109/TPWRS.2016.2569603.
  50. [49] ] R. Ramachandran and M. Arun, “Sensitivity based optimal real power rescheduling for congestion management using black hole algorithm,” Aust. J. Basic Appl. Sci., vol. 10, no. 15, pp. 183–93, October 2016.
  51. [50] S. Mandal, G. Das, K. K. Mandal, and B. Tudu, “A new improved hybrid algorithm for congestion management in a deregulated electricity industry using chaos enhanced differential evolution,” in 3rd IEEE International Conference on, 2017, https://doi.org/10.1109/CIACT.2017.7977386
  52. [51] U. Sultana, A. Khairuddin, A. S. Mokhtar, S. H. Qazi, and B. Sultana, “An optimization approach for minimizing energy losses of distribution systems based on distributed generation placement,” J. Teknol., vol. 79, no. 4, pp. 87–96, 2017, https://doi.org/10.11113/jt.v79.5574.
  53. [52] J. R. Chintam and M. Daniel, “Real-power rescheduling of generators for congestion management using a novel satin bowerbird optimization algorithm,” Energies, vol. 11, no. 1, p. 183, 2018, https://doi.org/10.3390/en11010183.
  54. [53] J. Salehi and A. Abdolahi, “Optimal scheduling of active distribution networks with penetration of PHEV considering congestion and air pollution using DR program,” Sustain. Cities Soc., vol. 51, p. 101709, 2019, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2019.101709.
  55. [54] S. R. Salkuti and S. C. Kim, “Congestion management using multiobjective glowworm swarm optimization algorithm,” J. Electr. Eng. Technol., vol. 14, pp. 1565–75, 2019, https://doi.org/10.1007/s42835-019-00206-w
  56. [55] R. Saranya, K. Balamurugan, and M. Karuppasamy, “Artificial bee colony algorithm based congestion management in restructured power system,” Indian J. Sci. Technol., vol. 8, no. April, pp. 171–8, 2015, https://doi.org/10.17485/ijst/2015/v8iS7/64298.
  57. [56] A. A. J. Basha, “Transmission congestion management in restructured power system using firefly algorithm,” Int. J. Comput.Appl., vol. 85, no. January, pp. 39–43, 2014.
  58. [57] S. Verma, S. Saha, and V. Mukherjee, “Optimal rescheduling of real power generation for congestion management using teachinglearning-based optimization algorithm,” J. Electr. Syst. Inf. Technol., vol. 5, no. 3, pp. 889–907, 2016, https://doi.org/10.1016/j.jesit. 2016.12.008.
  59. [58] V. Mukherjee and S. Verma, “Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser,” IET Gener. Transm. Distrib., vol. 10, no. 10, pp. 2548–61, 2016, https://doi.org/10.1049/iet-gtd.2015.1555.
  60. [59] K. Balamurugan, R. Muralisachithanandam, and V. Dharmalingam, “Electrical Power and Energy Systems Performance comparison of evolutionary programming and differential evolution approaches for social welfare maximization by placement of multi type FACTS devices in pool electricity market,” Int. J. Electr. Power Energy
  61. Syst., vol. 67, pp. 517–28, 2015, https://doi.org/10.1016/j.ijepes.2014.12.007.
  62. [60] S. T. Suganthi, D. Devaraj, K. Ramar, and S. Hosimin Thilagar, “An Improved Differential Evolution algorithm for congestion management in the presence of wind turbine generators,” Renew. Sustain. Energy Rev., vol. 81, no. June 2017, pp. 635–42, 2018,https://doi.org/10.1016/j.rser.2017.08.014.
  63. [61] S. Balaraman and N. Kamaraj, “Application of differential evolution for congestion management in power system,” Mod. Appl. Sci. vol. 4, no. 8, August 2010, https://doi.org/10.5539/mas.v4n8p33.
  64. [62] S. M. H. Nabavi, A. Kazemi, and M. A. S. Masoum, “Congestion management using genetic algorithm in deregulated power environments,” Int. J. Comput. Appl., vol. 18, no. 2, pp. 19–23, 2011, https://doi.org/10.5120/2257-2894.
  65. [63] S. Balaraman, “Congestion management in deregulated power system using real coded genetic,” Int. J. Eng. Sci. Technol., vol. 2,no. 11, pp. 6681–90, 2010.
  66. [64] S. S. Reddy, M. S. Kumari, and M. Sydulu, “Congestion management in deregulated power system by optimal choice and allocation of FACTS controllers using multi-objective genetic algorithm,” in IEEE PES T&D 2010, 2010, pp. 1–7, https://doi.org/10.1109/TDC.2010.5484520.
  67. [65] S. Pal, S. Sen, and S. Sengupta, “Power network reconfiguration for congestion management and loss minimization using genetic algorithm,” in Michael Faraday IET International Summit 2015, 2015, pp. 291–6, https://doi.org/10.1049/cp.2015.1646.
  68. [66] R. Peesapati, V. K. Yadav, and N. Kumar, Flower Pollination Algorithm Based Multi-Objective Congestion Management Considering Optimal Capacities of Distributed Generations. Elsevier B.V.; 2018
  69. [67] B. Bhattacharyya and V. K. Gupta, “Fuzzy based evolutionary algorithm for reactive power optimization with FACTS devices,” Int. J. Electr. Power Energy Syst., vol. 61, no. 1, pp. 39–47, 2014, https://doi.org/10.1016/j.ijepes.2014.03.008.
  70. [68] S. N. Pandey, S. Tapaswi, and L. Srivastava, “Integrated evolutionary neural network approach with distributed computing for congestion management,” Appl. Soft Comput. J., vol. 10, no. 1, pp. 251–60, 2010, ISSN: 1568-4946, https://doi.org/10.1016/j.asoc.2009.07.008.
  71. [69] B. K. Panigrahi and V. Ravikumar Pandi, “Congestion management using adaptive bacterial foraging algorithm,” Energy Convers. Manag., vol. 50, no. 5, pp. 1202–9, 2009, https://doi.org/10.1016/j.enconman.2009.01.029.
  72. [70] A. Ramesh Kumar and L. Premalatha, “Security constrained multi-objective congestion management in transactional based restructured electrical network using bacterial foraging algorithm,” in 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), 2013, pp. 63–7, https://doi.org/10.1109/ICCPCT.2013.6528913.
  73. [71] T. Mohanapriya and T. R. Manikandan, “Congestion management of deregulated electricity market using locational marginal pric- ing,” pp. 6–9, 2014
  74. [72] S. Huang, Q. Wu, S. S. Oren, R. Li, and Z. Liu, “Distribution locational marginal pricing through quadratic programming for congestion management in distribution networks,” IEEE Trans. Power Syst., vol. 30, no. 4, pp. 2170–8, 2015, https://doi.org/10. 1109/TPWRS.2014.2359977
  75. [73] Y. R. Sood, N. P. Padhy, H. O. Gupta. Deregulated model and locational marginal pricing. Electr. Power Syst. Res., vol. 77, pp. 574–82, 2007
  76. [74] M. Packiasudha, S. Suja, and J. Jerome, “Electrical Power and Energy Systems A new Cumulative Gravitational Search algorithm for optimal placement of FACT device to minimize system loss in the deregulated electrical power environment,” Int. J. Electr. Power Energy Syst., vol. 84, pp. 34–46, 2017, https://doi.org/10.1016/j. ijepes.2016.04.049
  77. [75] K. Kavitha and R. Neela, “Optimal allocation of multi-type FACTS devices and its effect in enhancing system security using BBO, WIPSO & PSO,” J. Electr. Syst. Inf. Technol., vol. 5, no. 3, pp. 777–93, 2018, https://doi.org/10.1016/j.jesit.2017.01.008
  78. [76] H. Y. Yamin and S. M. Shahidehpour, “Transmission congestion and voltage profile management coordination in competitive electricity markets,” Int. J. Electr. Power Energy Syst., vol. 25, no. 10, pp. 849–61, 2003, https://doi.org/10.1016/S0142-0615(03)00070-X
  79. [77] A. Ott, “PJM: A full service ISO market evolution (Panel on evolution of electricity market structures),” in 1999 IEEE Power Engineering Society Summer Meeting, PES 1999 – Conference Proceedings, 1999, https://doi.org/10.1109/PESS.1999.787410
  80. [78] P. Holmberg and E. Lazarczyk, “Congestion management in electricity networks: Nodal, zonal and discriminatory pricing (March 27, 2012),” IFN Working Paper, No. 915, SSRN: https:// ssrn.com/abstract=2055655, http://dx.doi.org/10.2139/ssrn. 2055655iitiyiiy
  81. [79] M. Sarwar and A. S. Siddiqui, “An approach to locational marginal price based zonal congestion management in deregulated electricity market,” Front. Energy, vol. 10, pp. 240–8, 2016, https:// doi.org/10.1007/s11708-016-0404-z
  82. [80] E. Muneender and D. M. V. Kumar, “Optimal rescheduling of real and reactive powers of generators for zonal congestion manage- ment based on FDR PSO,” in 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009, pp. 1–6, https:// doi.org/10.1109/TD-ASIA.2009.5356989.
  83. [81] A. Kumar and C. Sekhar, “DSM based congestion management in pool electricity markets with FACTS devices,” Energy Proced., vol.14, pp. 94–100, 2012, https://doi.org/10.1016/j.egypro.2011.12.901.
  84. [82] F. Zaeim-Kohan, H. Razmi, and H. Doagou-Mojarrad, “Multiobjective transmission congestion management considering demand response programs and generation rescheduling,” Appl. Soft Comput. J., vol. 70, pp. 169–81, 2018, https://doi.org/10.1016/j.asoc.2018.05.028.
  85. [83] Y. R. Sood and R. Singh, “Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources,” Renew. Energy, vol. 35, no. 8, pp. 1828–36, 2010, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2010.01.002.
  86. [84] A. Kumar and C. Sekhar, “Comparison of Sen Transformer and UPFC for congestion management in hybrid electricity markets,” Int. J. Electr. Power Energy Syst., vol. 47, no. 1, pp. 295–304, 2013, https://doi.org/10.1016/j.ijepes.2012.10.057.
  87. [85] ] A. Yousefi, T. T. Nguyen, H. Zareipour, and O. P. Malik, “Congestion management using demand response and FACTS devices,” Int. J. Electr. Power Energy Syst., vol. 37, no. 1, pp. 78–85, 2012, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2011.12.008.
  88. [86] M. Mahmoudian Esfahani, A. Sheikh, and O. Mohammed, “Adaptive real-time congestion management in smart power systems using a real-time hybrid optimization algorithm,” Electr. Power Syst. Res., vol. 150, pp. 118–28, 2017, https://doi.org/10. 1016/j.epsr.2017.05.012.
  89. [87] M. A. Paqaleh, A. A. Tehrani Fard, M. Rashidinejad, and K. Y. Lee, Optimal Placement and Sizing of Distributed Resources for Congestion Management considering Cost/benefit Analysis. IEEE PES, General Meeting, Minneapolis:-MN, 2017, https://doi.org/10.1109/JPROC.2010.2066250
  90. [88] A. K. Singh and S. K. Parida, “Congestion management with distributed generation and its impact on electricity market,” Int. J. Electr. Power Energy Syst., vol. 48, pp. 39–47, 2013, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2012.11.025.
  91. [89] A. Tabandeh, A. Abdollahi, and M. Rashidinejad, “Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures,” Energy, vol. 104, pp. 213–28, 2016, https://doi.org/10.1016/j.energy.2016.03.118
  92. [90] E. Dehnavi and F. Aminifar, “Congestion management through distributed generations and energy storage systems,” Int. Trans.Electr. Energy Syst., vol. 29, no. 3, p. e12018, 2019, https://doi.org/10.1002/2050-7038.12018
  93. [91]FERC, Assesement of Demand Response and Advanced Metring; 2006.
  94. [92] J. Wu, B. Zhang, and Y. Jiang, “Optimal day-ahead demand response contract for congestionmanagement in the deregulated powermarket considering wind power,” IET Gener. Transm. Distrib., vol. 12, no. 4, pp. 917–26, 2018, https://doi.org/10.1049/iet-gtd.2017.1063
  95. [93] H. J. Jabir, J. Teh, D. Ishak, and H. Abunima, “Impacts of demand-side management on electrical power systems: A review,” Energies. 2018, https://doi.org/10.3390/en11051050
  96. [94] J. Nikoukar and M. R. Haghifam, “Transmission cost allocation based on the use of system and considering the congestion cost,” Int. J. Electr. Power Energy Syst., vol. 43, no. 1, pp. 961–8, ISSN: 0142-0615, 2012, https://doi.org/10.1016/j.ijepes.2012.06.016.
  97. [95] J. Liu, M. M. A. Salama, and R. R. Mansour, “Identify the impact of distributed resources on congestion management,” IEEETrans. Power Deliv., vol. 20, no. 3, pp. 1998–2005, July 2005, https://doi.org/10.1109/TPWRD.2004.843401.
  98. [96] M. A. L_opez, S. Mart_ın, J. A. Aguado, and S. De La Torre, “V2G strategies for congestion management in microgrids with high penetration of electric vehicles,” Electr. Power Syst. Res., vol. 104, pp. 28–34, 2013, ISSN 0378-7796, https://doi.org/10.1016/j.epsr.2013.06.005.
  99. [97] J. A. P. Lopes, F. J. Soares, and P. M. R. Almeida, “Integration of electric vehicles in the electric power system,” Proc. IEEE, vol. 99, no. 1, pp. 168–83, January 2011, https://doi.org/10.1109/JPROC. 2010.2066250
  100. [98] J. Hu, C. Si, M. Lind, and R. Yu, “Preventing distribution grid congestion by integrating indirect control in a hierarchical electric vehicles’ management system,” IEEE Trans. Transp. Electrif., vol. 2, no. 3, pp. 290–9, September 2016, https://doi.org/10.1109/TTE.2016.2554469.
  101. [99] T. Nireekshana, G. Kesava Rao, and S. Sivanaga Raju, “Available transfer capability enhancement with FACTS using Cat Swarm Optimization,” Ain Shams Eng. J., vol. 7, no. 1, pp. 159–67, 2016, ISSN 2090-4479, https://doi.org/10.1016/j.asej.2015.11.011.

Read more of it

Subscribe to one journal or build your own list of several. One invoice, one payment, access for a full year.

Subscribe to journals →

Publish your article

Peer-reviewed journals across every subject, with open access options. Send your manuscript to the right editor today.

Publish with us → Calculate your APC
Support